The Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) was assembled in 2012 to increase our understanding of lower urinary tract symptoms (LUTS) by identifying important subtypes of patients with LUTS, and improving the measurement of patient experiences of LUTS. The Network's approach to defining patient subtypes was based on a re-sampling-based consensus clustering approach using self- reported patient data, resulting in the identification of novel LUTS-based clusters that are statistically and clinically distinct. The approach to improving the measurement of patient reports of LUTS was to systematically develop a new, high-quality item bank based on qualitative input from patient, community participants, internists, urologists, urogynecologists, and clinical researchers. Finally, in order to understand some of the pathophysiologic basis underlying lower urinary tract dysfunction, biologic information was obtained and analyzed from patient samples and imaging. After a successful initial 5-year funding cycle, LURN is prepared to build on the knowledge gained and take the next steps with the following Specific Aims: 1) To test and refine the original clustering model with a cohort including a wider range of symptom severity and a wider range of physiological measures, 2) To identify protein biomarker signatures contained within plasma that can be used to identify specific subgroups of men and women with LUTS 3) To determine phenotypic characteristics of women with lower urinary tract symptoms (LUTS) by measuring the functional components of the lower urinary tract, 4) To validate a comprehensive outcome tool for men and women with LUTS, and 5) To determine the role of psychosocial stress ? especially adverse childhood experiences ? in the severity and course of LUTS. The LURN II will recruit 1380 patients, stratified by sex. Our site will recruit 1/6 of these participants (N = 230). We have a multi-method approach to phenotyping patients with LUTS, which will include questionnaires, laboratory tests, mobile apps, and urodynamics of the bladder and urethra. Data will be analyzed using resampling-based cluster analyses, as well as longitudinal modelling of symptoms over time. We hypothesize that our biopsychosocial approach to assessing patients with LUTS will yield clinically- meaningful patient clusters, which in turn can be linked to causal mechanisms as well as treatment options. Moreover, we hypothesize that modifiable risk factors will be related to the course of LUTS over time, creating novel avenues for treatment. The impact of this study will lend itself to an improved understanding of the causes and nature of LUTS, which will set the stage for clinical trials to improve quality of life for these patients.